Knowledge-Based Scoring Functions in Drug Design: 2. Can the Knowledge Base Be Enriched?
Knowledge-Based Scoring Functions in Drug Design: 2. Can the Knowledge Base Be Enriched?
复制标题
药物设计中基于知识的评分函数：2.知识库可以丰富吗？
DOI：
10.1021/ci1003431
复制
发表时间：
2011-02-01
影响因子：
5.6
通讯作者：
Jiang, Hualiang
中科院分区：
文献类型：
--
作者：
Shen, Qiancheng;Xiong, Bing;Jiang, Hualiang
快速准确地预测大量不同蛋白质配体复合物的结合亲和力是药物发现中一项重要但极具挑战性的任务。开发基于知识的评分功能，利用已知的蛋白质配体复合物的结构信息，代表了一个有价值的贡献，这样的计算预测。在这项研究中，我们报告了一个名为IPMF的评分函数，它将额外的实验结合亲和力信息集成到提取的潜力，假设与“丰富”的知识库的评分函数可以实现结合亲和力预测的准确性增加。在我们的方法中，PMF 04的功能和原子类型被继承以隐式地捕获难以明确建模的结合效应，并且设计了一种新颖的迭代装置以逐渐定制初始势。我们用219种蛋白质配体复合物评估了所得IPMF的性能，并将其与计算机辅助药物设计中常用的7种评分函数进行了比较，包括GLIDE，AutoDock 4，维纳，PLP，LUDI，PMF和PMF 04。虽然IPMF在对天然或接近天然构象进行排名方面仅取得了中等成功，但它从测量的抑制亲和力中产生了1.41 log K-i/K-d单位的最低平均误差和测试集的最高Pearson相关系数R-p（2）0.40。这些结果证实了我们最初的假设“丰富”的知识基础的作用。随着公共领域中高质量结构和相互作用数据的快速增长，这项工作标志着朝着提高结合亲和力预测中基于知识的评分函数的准确性迈出了积极的一步。
Fast and accurate predicting of the binding affinities of large sets of diverse protein ligand complexes is an important, yet extremely challenging, task in drug discovery. The development of knowledge-based scoring functions exploiting structural information of known protein ligand complexes represents a valuable contribution to such a computational prediction. In this study, we report a scoring function named IPMF that integrates additional experimental binding affinity information into the extracted potentials, on the assumption that a scoring function with the "enriched" knowledge base may achieve increased accuracy in binding affinity prediction. In our approach, the functions and atom types of PMF04 were inherited to implicitly capture binding effects that are hard to model explicitly, and a novel iteration device was designed to gradually tailor the initial potentials. We evaluated the performance of the resultant IPMF with a diverse set of 219 protein ligand complexes and compared it with seven scoring functions commonly used in computer-aided drug design, including GLIDE, AutoDock4, VINA, PLP, LUDI, PMF, and PMF04. While the IPMF is only moderately successful in ranking native or near native conformations, it yields the lowest mean error of 1.41 log K-i/K-d units from measured inhibition affinities and the highest Pearson's correlation coefficient of R-p(2) 0.40 for the test set. These results corroborate our initial supposition about the role of "enriched" knowledge base. With the rapid growing volume of high quality structural and interaction data in the public domain, this work marks a positive step toward improving the accuracy of knowledge based scoring functions in binding affinity prediction.